Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. For out-of-sample segmentation, we compare the results with atlas-based segmentation, as well as test the hypothesis that using the phase image as input can improve the robustness. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report provides an evaluation of computational methods estimating neural structure.

Automatic Geometry-based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images.

The locus coeruleus (LC) is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the LC is a crucial step in quantitative non-invasive analysis of the LC in large MRI cohorts. Most publicly available imaging databases for training automatic LC segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI. Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences. In this work, we evaluate the feasibility of using non-contrast neuroanatomical information to geometrically approximate the LC region from standard 3-Tesla T1-weighted images of 20 subjects from the Human Connectome Project (HCP). We employ this dataset to train and internally/externally evaluate two automatic localization methods, the Expected Label Value and the U-Net. We also test the hypothesis that using the phase image as input can improve the robustness of out-of-sample segmentation. We then apply our trained models to a larger subset of HCP, while exploratorily correlating LC imaging variables and structural connectivity with demographic and clinical data. This report contributes and provides an evaluation of two computational methods estimating neural structure.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm.

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.

A high-resolution dataset on the plastic material flows in Switzerland.

A material flow analysis of the main plastic types used and arising as waste in Switzerland in 2017 is conducted, including consideration of stock change. Seven main plastic application segments are distinguished (packaging; building and construction; automotive; electrical and electronic equipment; agriculture; household items, furniture, leisure and others; and textiles), further divided into 54 product subsegments. For each segment, the most commonly used plastic types are considered, in total including eleven plastic types (HDPE, LDPE, PP, PET, PS, PVC, ABS, HIPS, PA, PC, and PUR). All product life cycle stages are regarded, including the determination of the product subsegments in which the individual post-consumer secondary materials obtained from mechanical recycling are applied. The underlying data are gathered from official statistics and administrative databases, scientific literature, reports by industry organizations and research institutions, websites, and personal communication with stakeholders. The compiled data are then reconciled. All flow data are provided and depicted in two Sankey diagrams: one diagram shows the material flows on a product-subsegment level and the second one on a plastic-type level. Users may retrieve the data with a script and transfer them into a relational database. The present material flow analysis data are used as a basis for the scenario analysis in Klotz et al. [1]. Besides scenario modelling, the data can be used in conducting life cycle assessments. Both utilizations can serve as a support for designing future plastic flow systems.

The effect of the European Industrial Emissions Directive on the air emission limit values set by competent authorities in the permitting procedure: The case of the Spanish cement industry.

The concept of the "flexibility principle" introduced by the Integrated Pollution Prevention and Control Directive in the procedure to determine the emission limit values in the permit raised the interest of several scholars and heightened the debate on environmental regulation. The Integrated Pollution Prevention and Control Directive allows a considerable flexibility to competent authorities in the permitting process to deviate from the use of best available techniques described in the reference documents. Several studies show how this flexibility lead to disparities in the permitting process among Member States, which reduced the potential benefits in the environmental performance of companies. After the adoption of the Industrial Emissions Directive by the Member States of the European Union, the permit contents of around 52,000 of the largest European Union industrial installations need to be updated by competent authorities to meet the requirements of this Directive. Several studies on its effectiveness have been developed to determine how emission limit values are set by competent authorities with reference to the range of emission levels associated with the best available techniques. This paper is oriented to study the effect of the Industrial Emissions Directive at sector level, investigating its effects on the air emission limit values set by the competent authorities in the permits for cement facilities. This paper contributes to the scientific debate in relation to the effect of these Directives on the environmental performance of the industrial installations. The data analysed in the case study considered show that the emission limit values have been reduced in permits consistently over the past 10 years.

Expected Label Value Computation for Atlas-Based Image Segmentation.

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible transformations and compute the expected label value (ELV), thereby not relying merely on the transformation resulting from the registration. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to liver segmentation on a dataset of computed tomography (CT) images.

Application of AHP and corrective factors for the determination of best available techniques and emission limit values at installation level: A case study in four cement installations.

After the adoption of the Industrial Emissions Directive in the European Union, requirements regarding emission limit values were made legally binding, and the competent authorities shall ensure that they do not exceed the emission levels associated with the best available techniques. This paper describes a two-stage method for the determination of best available techniques (BAT) and emission limit values (ELV) at installation level, applicable to all industrial sectors covered by the IED and to all pollutants to air and to water. This new method may support competent authorities to implement BAT conclusions into the IED permits. The determination of BAT is based on the use of analytical hierarchy process, while the ELV is determined by using corrective factors based on consumption and emission indicators from the installation. The method is applied in a case study on four existing cement installations in the region of Andalucia (Spain).

Study on Impact Acoustic-Visual Sensor-Based Sorting of ELV Plastic Materials.

This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles' (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

Pollutant formation in the pyrolysis and combustion of Automotive Shredder Residue.

The present work has been carried out to verify the feasibility of thermal valorization of an automobile shredder residue (ASR). With this aim, the thermal decomposition of this waste has been studied in a laboratory scale reactor, analyzing the pollutants emitted under different operating conditions. The emission factors of carbon oxides, light hydrocarbons, PAHs, PCPhs, PCBzs, PBPhs, PCDD/Fs, dioxin-like PCBs and PBDD/Fs were determined at two temperatures, 600 and 850°C, and under different oxygen ratios ranging from 0 (pure pyrolysis) to 1.5 (over-stoichiometric oxidation). After analyzing all these compounds, we conclude that thermal valorization of ASR is a clean way to treat this waste.

Optimization of wet shaking table process using response surface methodology applied to the separation of copper and aluminum from the fine fraction of shredder ELVs.

With the purpose of reducing the waste generated by end-of-life vehicles (ELVs) by enhancing the recovery and recycling of nonferrous metals, an experimental study was conducted with the finest size fraction of nonferrous stream produced at an ELV shredder plant. The aim of this work was to characterize the nonferrous stream and to evaluate the efficiency of a gravity concentration process in separating light and heavy nonferrous metal particles that could be easily integrated in a ELV shredder plant (in this case study the separation explicitly addressed copper and aluminum separation). The characterization of a sample of the 0-10mm particle size fraction showed a mixture of nonferrous metals with a certain degree of impurity due to the present of contaminants such as plastics. The majority of the particles exhibited a wire shape, preventing an efficient separation of materials without prior fragmentation. The gravity concentration process selected for this study was the wet shaking table and three operating parameters of the equipment were manipulated. A full factorial design in combination with a central composite design was employed to model metals recovery. Two second order polynomial equations were successfully fitted to describe the process and predict the recovery of copper and aluminum in Cu concentrate under the conditions of the present study. The optimum conditions were determined to be 11.1° of inclination, 2.8L/min of feed water flow and 4.9L/min of wash water flow. All three final products of the wet shaking table had a content higher than 90% in relation to one of the metals, wherein a Cu concentrate product was obtained with a Cu content of 96%, and 78% of Cu recovery and 2% of Al recovery.

Evaluation of the potential of different high calorific waste fractions for the preparation of solid recovered fuels.

Solid recovered fuels constitute a valuable alternative for the management of those non-hazardous waste fractions that cannot be recycled. The main purpose of this research is to assess the suitability of three different wastes from the landfill of the local waste management company (COGERSA), to be used as solid recovered fuels in a cement kiln near their facilities. The wastes analyzed were: End of life vehicles waste, packaging and bulky wastes. The study was carried out in two different periods of the year: November 2013 and April 2014. In order to characterize and classify these wastes as solid recovered fuels, they were separated into homogeneous fractions in order to determine different element components, such as plastics, cellulosic materials, packagings or textile compounds, and the elemental analysis (including chlorine content), heavy metal content and the heating value of each fraction were determined. The lower heating value of the waste fractions on wet basis varies between 10 MJ kg(-1) and 42 MJ kg(-1). One of the packaging wastes presents a very high chlorine content (6.3 wt.%) due to the presence of polyvinylchloride from pipe fragments, being the other wastes below the established limits. Most of the wastes analyzed meet the heavy metals restrictions, except the fine fraction of the end of life vehicles waste. In addition, none of the wastes exceed the mercury limit content, which is one of the parameters considered for the solid recovered fuels classification. A comparison among the experimental higher heating values and empirical models that predict the heating value from the elemental analysis data was carried out. Finally, from the three wastes measured, the fine fraction of the end of life vehicles waste was discarded for its use as solid recovered fuels due to the lower heating value and its high heavy metals content. From the point of view of the heating value, the end of life vehicles waste was the most suitable residue with a lower heating value of 35.89 MJ kg(-1), followed by the packaging waste and the bulky waste, respectively. When mixing the wastes studied a global waste was obtained, whose classification as solid recovered fuels was NCV 1 Cl 3 Hg 3. From the empirical models used for calculating higher heating value from elemental content, Scheurer-Kestner was the model that best fit the experimental data corresponding to the wastes collected in November 2013, whereas Chang equation was the most approximate to the experimental heating values for April 2014 fractions. This difference is due to higher chlorine content of the second batch of wastes, since Chang equation is the only one that incorporates the chlorine content.

Interval linear programming model for long-term planning of vehicle recycling in the Republic of Serbia under uncertainty.

An interval linear programming approach is used to formulate and comprehensively test a model for optimal long-term planning of vehicle recycling in the Republic of Serbia. The proposed model is applied to a numerical case study: a 4-year planning horizon (2013-2016) is considered, three legislative cases and three scrap metal price trends are analysed, availability of final destinations for sorted waste flows is explored. Potential and applicability of the developed model are fully illustrated. Detailed insights on profitability and eco-efficiency of the projected contemporary equipped vehicle recycling factory are presented. The influences of the ordinance on the management of end-of-life vehicles in the Republic of Serbia on the vehicle hulks procuring, sorting generated material fractions, sorted waste allocation and sorted metals allocation decisions are thoroughly examined. The validity of the waste management strategy for the period 2010-2019 is tested. The formulated model can create optimal plans for procuring vehicle hulks, sorting generated material fractions, allocating sorted waste flows and allocating sorted metals. Obtained results are valuable for supporting the construction and/or modernisation process of a vehicle recycling system in the Republic of Serbia.

Fuzzy risk explicit interval linear programming model for end-of-life vehicle recycling planning in the EU.

End-of-life vehicles (ELVs) are vehicles that have reached the end of their useful lives and are no longer registered or licensed for use. The ELV recycling problem has become very serious in the last decade and more and more efforts are made in order to reduce the impact of ELVs on the environment. This paper proposes the fuzzy risk explicit interval linear programming model for ELV recycling planning in the EU. It has advantages in reflecting uncertainties presented in terms of intervals in the ELV recycling systems and fuzziness in decision makers' preferences. The formulated model has been applied to a numerical study in which different decision maker types and several ELV types under two EU ELV Directive legislative cases were examined. This study is conducted in order to examine the influences of the decision maker type, the α-cut level, the EU ELV Directive and the ELV type on decisions about vehicle hulks procuring, storing unprocessed hulks, sorting generated material fractions, allocating sorted waste flows and allocating sorted metals. Decision maker type can influence quantity of vehicle hulks kept in storages. The EU ELV Directive and decision maker type have no influence on which vehicle hulk type is kept in the storage. Vehicle hulk type, the EU ELV Directive and decision maker type do not influence the creation of metal allocation plans, since each isolated metal has its regular destination. The valid EU ELV Directive eco-efficiency quotas can be reached even when advanced thermal treatment plants are excluded from the ELV recycling process. The introduction of the stringent eco-efficiency quotas will significantly reduce the quantities of land-filled waste fractions regardless of the type of decision makers who will manage vehicle recycling system. In order to reach these stringent quotas, significant quantities of sorted waste need to be processed in advanced thermal treatment plants. Proposed model can serve as the support for the European vehicle recycling managers in creating more successful ELV recycling plans.

The exocyst and regulatory GTPases in urinary exosomes.

Cilia, organelles that function as cellular antennae, are central to the pathogenesis of "ciliopathies", including various forms of polycystic kidney disease (PKD). To date, however, the molecular mechanisms controlling ciliogenesis and ciliary function remain incompletely understood. A recently proposed model of cell-cell communication, called "urocrine signaling", hypothesizes that a subset of membrane bound vesicles that are secreted into the urinary stream (termed exosome-like vesicles, or ELVs), carry cilia-specific proteins as cargo, interact with primary cilia, and affect downstream cellular functions. This study was undertaken to determine the role of the exocyst, a highly conserved eight-protein trafficking complex, in the secretion and/or retrieval of ELVs. We used Madin-Darby canine kidney (MDCK) cells expressing either Sec10-myc (a central component of the exocyst complex) or Smoothened-YFP (a ciliary protein found in ELVs) in experiments utilizing electron gold microscopy and live fluorescent microscopy, respectively. Additionally, human urinary exosomes were isolated via ultracentrifugation and subjected to mass-spectrometry-based proteomics analysis to determine the composition of ELVs. We found, as determined by EM, that the exocyst localizes to primary cilia, and is present in vesicles attached to the cilium. Furthermore, the entire exocyst complex, as well as most of its known regulatory GTPases, are present in human urinary ELVs. Finally, in living MDCK cells, ELVs appear to interact with primary cilia using spinning disc confocal microscopy. These data suggest that the exocyst complex, in addition to its role in ciliogenesis, is centrally involved in the secretion and/or retrieval of urinary ELVs.

Characterization of Shredder Residues generated and deposited in Denmark.

This study presents a detailed characterization of Shredder residues (SR) generated and deposited in Denmark from 1990 to 2010. It represents approximately 85% of total Danish SR. A comprehensive sampling, size fractionation and chemical analysis was carried out on entire samples as well as on each individual size fraction. All significant elemental contents except oxygen were analyzed. The unexplained "balance" was subsequently explained by oxygen content in metal oxides, carbonates, sulphates and in organics, mainly cellulose. Using mass and calorific balance approaches, it was possible to balance the composition and, thereby, estimate the degree of oxidation of elements including metals. This revealed that larger fractions (>10mm, 10-4mm, 4-1mm) contain significant amount of valuable free metals for recovery. The fractionation revealed that the >10mm coarse fraction was the largest amount of SR being 35-40% (w/w) with a metal content constituting about 4-9% of the total SR by weight and the <1mm fine fraction constituted 27-37% (w/w) of the total weight. The lower heat value (LHV) of SR samples over different time periods (1990-2010) was between 7 and 17 MJ/kg, declining with decreasing particle size. The SR composition is greatly dependent on the applied shredding and post shredding processes at the shredding plants causing some variations. There are uncertainties related to sampling and preparation of samples for analyses due to its heterogeneous nature and uncertainties in the chemical analyses results (≈15-25%). This exhaustive characterization is believed to constitute hitherto the best data platform for assessing potential value and feasibility of further resource recovery from SR.